Genetic Algorithm Based Auditory Training

ABSTRACT

Embodiments of the present invention are generally directed to the use of a genetic algorithm for the purpose of providing progressive and adaptive auditory training (rehabilitation) to a recipient of a hearing prosthesis. In general, the genetic algorithm is used to adapt the training process to automatically increase the difficulty of the training based on recipient feedback and performance. That is, the genetic algorithm progressively removes perceivable sounds from the training process so as to generate groups of sounds that are difficult for a recipient to perceive.

BACKGROUND

1. Field of the Invention

The present invention relates generally to auditory training, and moreparticularly, to genetic algorithm based auditory training.

2. Related Art

Hearing loss, which may be due to many different causes, is generally oftwo types, conductive and/or sensorineural. Conductive hearing lossoccurs when the normal mechanical pathways of the outer and/or middleear are impeded, for example, by damage to the ossicular chain or earcanal. Sensorineural hearing loss occurs when there is damage to theinner ear, or to the nerve pathways from the inner ear to the brain.

Several types of hearing prosthesis have been developed to treatconductive hearing loss by generating mechanical motion of a recipient'scochlea fluid. These hearing prostheses may include, for example,acoustic hearing aids, bone conduction devices, implantable mechanicalstimulators, etc. The selection of a specific type of hearing prosthesisfor use by a recipient may be based on the recipient's degree ofresidual hearing, age, or other factors.

Those suffering from certain forms of sensorineural hearing loss areunable to derive suitable benefit from hearing prostheses that generatemechanical motion of the cochlea fluid. However, such individuals maybenefit from hearing prostheses that stimulate nerve cells of therecipient's auditory system in other ways (e.g., electrical, optical andthe like). Cochlear implants are often proposed when the sensorineuralhearing loss is due to the absence or destruction of the cochlea haircells, which transduce acoustic signals into nerve impulses. Auditorybrainstem implants may also be proposed when a recipient experiencessensorineural hearing loss resulting from damage to the auditory nerve,which forwards signals from the cochlea to the brain.

When hearing prostheses are first used by a recipient, nerve pulses willoccur in the recipient's auditory nerve and brain causing a hearingsensation. However, certain recipients may have difficulty interpretingthese nerve pulses and do not correctly perceive the sound. As such,recipients of hearing prostheses typically receive some form of auditorytraining that, over time, enables the recipients to discriminate betweendifferent sounds and to attach meaning to those sounds.

SUMMARY

In one aspect of the invention, a method is provided. The methodcomprises selecting an initial set of auditory training sound tokensfrom an auditory training library, presenting the initial set of soundtokens to a recipient via a hearing prosthesis, receiving recipientfeedback in response to the presented sound tokens, wherein the feedbackindicates the recipient's perception of the sound tokens, and executinga genetic algorithm based on the recipient's perception of the soundtokens to create a second set of auditory sound tokens for presentationto the recipient.

In another aspect of the present invention, a method is provided. Themethod comprises selecting a set of parameter values for a cochlearimplant, providing the set of parameter values to the cochlear implantfor use in delivering stimulation to a recipient, and executing agenetic algorithm based auditory training process in which sound tokensare processed and delivered to the recipient in accordance with the setof parameter values.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are described herein in conjunctionwith the accompanying drawings, in which:

FIG. 1 is a schematic diagram of a cochlear implant that may be used inconjunction with embodiments of the present invention;

FIG. 2 is a schematic diagram of an auditory training library inaccordance with embodiments of the present invention;

FIG. 3 is a schematic diagram of an initial generation of sound tokensfor delivery to a recipient in accordance with embodiments of thepresent invention;

FIG. 4 is a flowchart of an auditory training method in accordance withembodiments of the present invention;

FIG. 5 is a block diagram of an auditory training system in accordancewith embodiments of the present invention;

FIGS. 6A-7B illustrate example display screens that may be provided to arecipient during auditory training in accordance with embodiments of thepresent invention; and

FIG. 8 is a flowchart of a method in accordance with embodiments of thepresent invention.

DETAILED DESCRIPTION

Embodiments of the present invention are generally directed to the useof a genetic algorithm (GA) for the purpose of providing progressive andadaptive auditory training (rehabilitation) to a recipient of a hearingprosthesis. In general, the genetic algorithm is an interactiveaugmented genetic algorithm (IAGA) that is used to adapt the trainingprocess to automatically increase the difficulty of the training basedon recipient feedback and performance. That is, the IAGA progressivelyremoves perceivable sounds from the training process so as to generategroups of sounds that are difficult for a recipient to perceive.

A standard or traditional genetic algorithm is, in general, an adaptiveprocedure that implements aspects of biological evolution including, forexample, “natural selection,” “procreation with inheritance,” and“random mutation,” among others. The underlying premise of a traditionalgenetic algorithm is that the evolutionary process will, over multiplegenerations, produce an optimal “organism.”

Genetic algorithms are capable of evolving surprisingly complex andinteresting structures. Such structures may represent not only solutionsto problems, but also strategies for playing games, visual images, oreven simple computer programs. The Darwinian theory of evolution depictsbiological systems as the product of the ongoing process of naturalselection. Likewise, genetic algorithms allow the utilization ofcomputing devices to evolve solutions over time, instead of designingthem by hand. Because almost any method, theory, or technique can beprogrammed on a computing device, this implies an approach to problemsolving that can be, at least partially, automated by a computer.

Hearing prostheses may include, for example, acoustic hearing aids, boneconduction devices, mechanical stimulators, auditory brain stimulatorscochlear implants, mixed-mode devices, etc. It is to be appreciated thatgenetic algorithm based auditory training in accordance with embodimentsof the present invention may be used in connection with any of the aboveor other hearing prostheses. However, merely for ease of description,embodiments of the genetic algorithm based auditory training areprimarily described herein in connection with one exemplary hearingprosthesis, namely a cochlear implant (also commonly referred to ascochlear implant device, cochlear prosthesis, and the like; simply“cochlear implant” herein).

FIG. 1 is perspective view of an exemplary cochlear implant 100 that maybe used in conjunction with genetic algorithm based auditory training inaccordance with embodiments of the present invention. Cochlear implant100 comprises an external component 142 and an internal or implantablecomponent 144. The external component 142 is directly or indirectlyattached to the body of the recipient and typically comprises one ormore sound input elements 124 (e.g., microphones, telecoils, etc.) fordetecting sound, a sound processor 126, a power source (not shown), anexternal coil 130 and, generally, a magnet (not shown) fixed relative tothe external coil 130. The sound processor 126 processes electricalsignals generated by a sound input element 124 that is positioned, inthe depicted embodiment, by auricle 110 of the recipient. The soundprocessor 126 provides the processed signals to external coil 130 via acable (not shown).

The internal component 144 comprises an elongate stimulating assembly118, a stimulator unit 120, and an internal receiver/transceiver unit132, sometimes referred to herein as transceiver unit 132. Thetransceiver unit 132 is connected to an internal coil 136 and,generally, a magnet (not shown) fixed relative to the internal coil 136.Internal transceiver unit 132 and stimulator unit 120 are sometimescollectively referred to herein as a stimulator/transceiver unit.

The magnets in the external component 142 and internal component 144facilitate the operational alignment of the external coil 130 with theinternal coil 136. The operational alignment of the coils enables theinternal coil 136 to transmit/receive power and data to/from theexternal coil 130. More specifically, in certain examples, external coil130 transmits electrical signals (e.g., power and stimulation data) tointernal coil 136 via a radio frequency (RF) link. Internal coil 136 istypically a wire antenna coil comprised of multiple turns ofelectrically insulated single-strand or multi-strand platinum or goldwire. The electrical insulation of internal coil 136 is provided by aflexible silicone molding. In use, transceiver unit 132 may bepositioned in a recess of the temporal bone of the recipient. Variousother types of energy transfer, such as infrared (IR), electromagnetic,capacitive and inductive transfer, may be used to transfer the powerand/or data from an external device to cochlear implant and FIG. 1illustrates only one example arrangement.

Elongate stimulating assembly 118 has a proximal end connected to thestimulator unit 120 and a distal end implanted in cochlea 140 throughthe use of the implantation imaging techniques presented herein.Elongate stimulating assembly 118 also includes a contact array 146 thatcomprises a plurality of stimulating contacts 148 that may be electricaland/or optical contacts. Stimulating assembly 118 extends fromstimulator unit 120 to cochlea 140 through mastoid bone 119 and acochleostomy 122.

As noted, a genetic algorithm is an adaptive procedure that implementsaspects of evolution that, through multiple iterations, produces anorganism that is best suited for its environment. An iteration of agenetic algorithm begins with a generation of organisms (“parents”) thatare used to produce a succeeding generation of organisms (“children”).This typically involves two steps, namely selection and procreation.Selection involves the choosing of a subset of organisms as thepotential parents of the organisms of the succeeding generation (thechildren). Procreation involves the creation of children from theselected sets of potential parents.

Traditionally, binary strings of zeros (0s) and ones (1s) have been usedto represent organisms within a genetic algorithm (both parents andchildren). Merely for ease of illustration, traditional binary bitstrings will also be used herein to represent organisms. However, asdescribed further below, other encodings are also possible and may beused in alternative embodiments of the present invention. For example,other variations organisms are: lists of numbers indexed into aninstruction table, nodes in a linked list, hashes, objects, etc.

The basic idea of the genetic algorithm is that first a population oforganisms is created in a computing device (typically with genes storedas binary strings in the device's memory), and then the population isevolved with use of the principles of variation, selection, andinheritance. That is, selection operates on strings of binary digitsstored in the memory of a computing device, and over time, thefunctionality of these strings evolves in much the same way that thedeoxyribonucleic acid (DNA) of species naturally evolve. There are manyways of implementing a genetic algorithm, but the most basic is thatsuggested by J. H. Holland, in Adaptation in Natural and ArtificialSystems, Univ. of Michigan Press, Ann Arbor, Mich., 1975, reprinted byMIT Press, Cambridge, Mass., 1992, which is hereby incorporated byreference herein.

In order to execute the selection process of a genetic algorithm, eachorganism in a generation is first assigned a fitness value throughexecution of a fitness function. On the basis of these fitness values,the selection function evaluates the organisms. After selection, geneticoperators are applied probabilistically. For example, some organisms mayhave bits in their genes mutated from a 1 to a 0 or a 0 to a 1, and/orparts of different organisms' genes may then be combined into new ones.The resulting population comprises the next generation and the processrepeats itself.

The fitness function is the primary place in which a genetic algorithmis tailored to a specific problem. Once all organisms in the populationof a particular generation have been evaluated, their fitness values areused as the basis for selection. Selection is implemented by eliminatinglow-fitness individuals from the population, and inheritance is oftenimplemented by passing on characteristics of high-fitness individuals tosubsequent ones. Genetic operators such as mutation (flipping individualbits) and crossover or inheritance (exchanging sub-strings of twoorganisms to obtain two offspring) are applied probabilistically to theselected individuals to produce new organisms. By replacing members ofthe old generation with such new organisms, new generations are producedso that the old generation is completely replaced (“synchronously”), orso that the new and old members of the generation overlap(“asynchronously”). The genetic operators have been shown to generatenew organisms that, on average, are better than the average fitness oftheir parent organisms. Therefore, when this cycle of evaluation,selection, and genetic operations is iterated for many generations, theoverall fitness of the population generally improves, on average, andthe organisms represent improved “solutions” to the specific problem.

Selection and reproduction can be performed in any of several ways. Forexample, the least fit organisms may be arbitrarily eliminated from thepopulation while copies of all the remaining organisms are made.Alternatively, organisms may be replicated in direct proportion to theirfitness or the fitness of the organisms may be scaled in any of severalways such that organisms are replicated in direct proportion to theirscaled values. Likewise, the crossover operator can pass on bothoffspring to the new generation, or it can arbitrarily choose one to bepassed on.

As noted, embodiments presented herein are directed to using a modifiedgenetic algorithm, referred to herein as an interactive augmentedgenetic algorithm (IAGA), during auditory training of a recipient of acochlear implant or other hearing prosthesis. Specifically, embodimentsmay be directed to executing an IAGA to select sounds for delivery tothe recipient during the training process in order to improve therecipient's sound perception. In essence, the IAGA is used to adapt theauditory training to filter out sounds that are easily perceived by auser and such that to automatically increase the difficulty of thetraining process based on the recipient's feedback.

For example, if it is determined that the auditory training is too easyfor the recipient, the difficulty of the auditory training may beincreased by, for example, reducing the signal to noise ratio, addingadditional noise sources (e.g., in the case of music, adding moreinstruments), and increasing the difficulty of a listeningdiscrimination task (e.g., moving from an obvious phoneme confusion suchas ‘k’ vs. ‘s’ to a more subtle one of ‘r’ vs. ‘l’). In practice, thesechanges may be gradual and occur through the implementation of thegenetic algorithm (i.e., the genetic algorithm is used to progressivelyand adaptively increase the difficulty of the auditory training).

As described further below, genetic algorithms used in accordance withembodiments of the present invention may be IAGAs. An IAGA is a modifiedversion of an interactive genetic algorithm (IGA) that explicitlyincorporates human feedback into the evolutionary process of the geneticalgorithm. An IAGA uses modifications to the standard genetic algorithmand/or IGA that are more consistent with psychometric theory andpractice. These modifications implemented in an IAGA may include, forexample, representation, generation, selection, crossover, mutation,stopping criteria, and initialization.

In one embodiment, the genetic algorithm operates to generate successivegenerations of multiple groups of sounds for delivery to the recipient.Recipient feedback during execution of such a genetic algorithm forms abasis for selecting sounds in each of the successive generations. Inother words, selection is based on the perceptual auditory judgment ofthe recipient during execution of the genetic algorithm. In eachgeneration, less than all (for example, half) of the groups of soundsare selected and used to determine a larger number of groups of soundsfor the next generation (e.g., twice as large, if it is desired that allgenerations be of the same size).

In embodiments, each organism of the auditory training genetic algorithmcorresponds to a sound file or recording (collectively referred to assound tokens) that may be played to a recipient through the cochlearimplant. Embodiments of the present invention may make use of a largenumber of different types of sound tokens during auditory training. Thecollection of possible sound tokens is referred to herein as an auditorytraining library.

In certain embodiments, the auditory training library includes soundtokens comprised of language/speech sounds such as consonant soundsformed based using different phonetic attributes including manner (e.g.,stop, fricative, affricate, nasal, liquid, glide), place (e.g.,bilabial, labiodental, linguadental, linguaalveolar, linguapalatal,linguavelar, glottal), voice (e.g., voiced or voiceless); vowels (e.g.,monophthongs or diphthongs), words, morphemes, and/or other formants orphonemes. The sound tokens may be further defined by combinations ofspeech characteristics. In further embodiments, sound tokens maycomprise parts of music (e.g., instruments, musical notes, pitches,lyrics, etc.) or tones. In certain embodiments described further below,the auditory training library includes recipient-specific sound tokens(e.g., a baby's cry, a spouse's voice, etc.) It is to be appreciatedthat the above sound tokens are merely exemplary and that a large numberof different types of sounds may be used during auditory training.

Each organism (sound token) in the auditory training library isrepresented by a set of N_(b) “genes” (bits) such that the number ofpossible unique organisms is 2^(Nb). In other words, each sound token inthe auditory training library is mapped to a unique binary bit stringthat is used for the mathematical operations. FIG. 2 is a schematicdiagram illustrating a subset of an auditory training library 200. FIG.2 illustrates fifteen (15) different sound tokens 202-230 and a portionof their corresponding representative bit strings. For ease ofdescription herein, sound tokens and their corresponding bit stringswill be referred to using the same reference numbers.

It is to be appreciated that a large number of sound tokens may requirethe use of a large number of distinct bit streams. As such, the bitstreams used in embodiments of the present invention may have differentlengths that depend on the number of sound tokens within the auditorytraining library. Merely for ease of illustration, only a portion ofeach bit string is shown in FIG. 2.

In certain embodiments, sound tokens may be randomly mapped to bitstrings. However, in other embodiments, the sound tokens are mapped tobit strings in a manner that represents similarity between sounds. Forexample, two sounds that sound similar may be mapped to sequential bitstrings.

It is to be appreciated that the use of binary bits to represent thesound tokens is merely for ease of illustration and that encodings arealso possible and may be used in alternative embodiments of the presentinvention. Further details of one such alternative encoding (i.e., analternative sound token representation) are provided below.

At the beginning of the genetic algorithm based auditory training, aninitial set of sound tokens are selected as the parent generation oforganisms for delivery to the recipient. FIG. 3 is schematic diagram ofone such example initial parent generation 250. In the embodiment ofFIG. 3, the parent generation 250 is comprised of five (5) organisms204, 230, 216, 206, and 220 selected from the auditory training library200. As noted, each organism represents a sound that may be delivered tothe recipient via the recipient's cochlear implant. It is to beappreciated that the number of organisms as well as the specificorganisms selected in FIG. 3 are merely illustrative and that differenttypes and numbers of organisms may be used in alternative embodiments.The selection of the initial sound tokens may be random or based onpreferences of the recipient, audiologists/clinicians, or may beselected from the results of a previous execution of the auditorytraining process.

As noted, a fitness function is executed to evaluate each of theorganisms in each generation of a genetic algorithm. The fitnessfunction identifies which organisms will survive to become parents andprocreate, and which of the organisms will die. Generally, the number ofsurvivors is constant from generation to generation, although that neednot be the case.

In accordance with embodiments presented herein, the fitness functionuses subjective feedback from the recipient. For example, a first one ofthe sound tokens (organisms), such as sound token 204, is presentedthrough the cochlear implant. The recipient is then asked to identifythe presented sound token 204 and the recipient's response is receivedand stored. This process continues until all, or a selected number, ofthe sound tokens in parent generation 250 have been presented to therecipient and corresponding recipient responses have been received

For sound tokens that are accurately identified by the recipient, it isdetermined that the recipient may need little or no further training onthat sound token (or similar tokens). However, if the recipient fails toaccurately identify a sound token, it is determined that the sound tokenis difficult for the recipient to perceive (comprehend) and that furthertraining on that sound token (or similar tokens) is desirable. As such,in the context of the auditory training genetic algorithm, the fitnessfunction is based on whether or not the recipient correctly perceives apresented sound token. Sound tokens that are correctly perceived aredetermined to be “unfit” (i.e., selected to die), while sound tokensthat are incorrectly perceived are selected to be “fit” (i.e., selectedto procreate).

The concept of generally eliminating understood sound tokens andselecting misunderstood sound tokens seems, as first, counterintuitivewhen viewed in the context of conventional genetic algorithms. However,it is important to understand that the ultimate goal of the techniquespresented herein are to improve the recipient's sound perception (i.e.,to teach the recipient to hear better with the cochlear implant). Assuch, the IAGA is used as a tool in this regard to adaptively andprogressively select sound tokens that challenge the user.Simultaneously, the IAGA substantially filters out sound tokens that aredetermined to be understood by the user.

It is to be appreciated that the IAGA may not permanently eliminate allunderstood sound tokens from subsequent generations. For example, inembodiments certain understood sound tokens may be re-presented to therecipient in a subsequent generation as a mechanism to ensure that therecipient understands that token (i.e., a double check of certain tokensmay be performed).

FIG. 4 is a flow chart of a genetic algorithm based auditory trainingmethod 448 in accordance with embodiments of the present invention.Additionally, merely for ease of illustration, FIG. 4 does notillustrate some of the enhancements described herein. It should beunderstood that the omission of enhancements in this example is merelyfor simplification, and other embodiments may employ each (or a subsetof) the enhancements described elsewhere herein.

Method 448 of FIG. 4 begins at block 450 where an initial generation ofeight sound tokens is selected. The selection of this initial generationmay be performed in a number of different manners. In one embodiment,the initial sound tokens are selected at random from an auditorytraining library. In another embodiment, the auditory training sessionmay be specifically directed to a specific purpose and the initial soundtokens are selected from only a subset if the auditory training library.For example, the auditory training session may be specifically directedto musical training and the initial sound tokens are all musical notes,pitches, lyrics, etc.

In one embodiment, diversity within the initial population is desired.Diversity may be defined as the average Hamming distance between thevarious sound tokens and ranges between 0 and 1, where a 1 indicatesmaximum diversity and a 0 indicates minimum diversity. If the diversityis below a threshold (e.g., 0.53), then the initial generation isdetermined to have an insufficient diversity and a new set of soundtokens may be selected. In certain embodiments, pre-selected soundtokens may also be included among the initial generation. Thesepre-selected sound tokens may be drawn from prior runs of the auditorytraining procedure, sound tokens from a recipient specific database ofsound tokens (e.g., recipient specific or recipient added sound tokens)or sound tokens selected by a clinician based on experience, suggestionsand recommendations from others, etc. The pre-selected sound tokensand/or the auditory training library may, for example, be stored inmemory internal or external to an auditory training system, such as atraining described in greater detail below.

Next, at block 452 a fitness function is executed on the initialgeneration. That is, the initial eight sound tokens are sequentiallyplayed to the recipient through the cochlear implant (e.g., via adedicated connection or indirectly over speakers). As described above,after each sound token is played, the recipient is asked to identify thesound token. In one embodiment, the auditory training is performed by,or in the presence of, a clinician who receives the recipient'sidentification of the sound token. The clinician may provide therecipient with several visual choices (e.g., in a book, on a screen of acomputing device, etc.) and the recipient is instructed to select whichpossible choice is closest to the sound that he/she perceived. Inanother embodiment, the auditory training is performed by the recipienton a computing device at a remote location (i.e., outside of aclinician's office). In such embodiments, the computing device isconfigured to visually display several choices and the user selectswhich possible choice is closest to the sound that he/she perceived.

After execution of the fitness function for all or a selected number ofsound tokens in the initial generation, a determination is made at block454 as to whether the genetic algorithm (and the auditory training)should be stopped (i.e., whether stopping criteria have been satisfied).In one embodiment, the auditory training is stopped after apredetermined number of generations. In another embodiment, the auditorytraining is stopped after a predetermined period of time.

If the stopping criteria are satisfied, then the auditory training endsat block 456. Otherwise, at block 458, a new generation is createdthrough reproduction as described elsewhere herein. The reproductionoccurs using primarily or only those sound tokens that the recipientfailed to correctly identify.

After creation of the new generation, the fitness function is againexecuted at block 452. The process of blocks 452, 454, and 458 continueiteratively until the stopping criteria are satisfied.

The method of the above-discussed embodiment has the advantage that itcan be automated, requiring little or no supervision by the clinician.It may also be repeated periodically as desired by the recipient toenhance speech perception. Separate optimizations may be performed forspecific classes of input signals (e.g., speech in quiet, speech innoise, music, etc.).

As noted above, the genetic algorithm used to progressively adapt theauditory training process may be an IAGA. In general, an IAGA modifiesboth the procedural and algorithmic components of an IGA to better matchthe human-selection process. IGAs explicitly incorporate human feedbackinto the evolutionary process of the genetic algorithm. In its simplestform, the IGA uses a subjective recipient-generated “goodness” responsein place of some objective function. Based on these responses, thepopulation is evolved using modifications to a standard (traditional)genetic algorithm. These modifications to the standard genetic algorithmresult in a framework that is more consistent with psychometric theoryand practice. Specially, modifications with respect to representation,generation, selection, crossover, mutation, stopping criteria andinitialization are introduced. These modifications define the IAGA.

At the core of the IGA is a measure of the human preference.Psychometric techniques for obtaining such preference judgments (e.g.,scaling or ranking) impose certain limits on general properties of anIGA. From the standpoint of memory load, the number of stimuli arecipient can judge at any one time is typically bounded by the “7±1”rule. While it is possible to exceed this bound, in practice, theclinician should provide some means for the participant to compare amongoptions as part of the response procedure. From the standpoint of taskload, recipients often fatigue after 1-2 hours of testing, whichimplicitly limits the number of generations in a run of the IGA andfavors testing procedures that do not allow the recipient the option ofreviewing or comparing members of the current generation.

Finally, from the standpoint of stimulus variation, humans are much morelikely to handle heavier memory and task loads if the artefacts they areevaluating have a sufficient degree of variability. This less-formalizedconcept in psychometrics respects the differences in performanceobserved, for example, when running fixed-level vs. adaptivepsychophysical methods. Fixed-level methods, in which the same stimuluscondition is repeated for 50-100 trials, may suffer from lags inattention, either because the discrimination or detection task is toodifficult (performance is near chance) or because it is too easy(performance is nearly perfect).

In contrast, sequential adaptive estimation methods, are better able tosustain the recipient's attention over 50-100 trials by varying thestimulus condition from trial to trial. This desire for a procedure witha sufficient degree of stimulus variation runs counter to the desire forhomogeneity within a current generation as an IGA-run evolves. Stimulusvariation also is known to be important in “teaching” the recipient todiscriminate among those properties of the stimulus which are relevantto the task from those which are not. Accordingly, as the populationhomogenizes over an IGA run, recipient's are more likely to attend tothose stimulus properties that make the scoring task manageable, ratherthan those that are indicative of potentially better variations. This isparticularly a problem when the participant may not really know, at theoutset of a run, what stimulus properties they prefer and only learnthese from the generated exemplars.

Whereas the three factors above reflect the insertion of the human inthe “feedback loop” of a genetic algorithm, psychometric theory alsopoints to the inherent limitations of how data generated by themeasurements themselves can be interpreted. In any sensory scaling task,the experimenter decides whether the data should be treated on acategorical, ordinal, interval or ratio scale. In general, withoutadditional assumptions or more elaborate psychometric procedures, it isrecognized that preference scores provided by a human recipient are nostronger than ordinal. Should the rules of parent selection assume thefigure of merit to be drawn from interval or ratio scales (as istypically the case in standard genetic algorithms), then the rulesshould be adjusted to accommodate the weaker ordinal or even categoricalnature of the data the recipient provides.

The consequences of the psychophysical limitations to the implementationof an IGA are that smaller, as opposed to larger, search spaces arelikely to yield valid results, and mechanics of the selection,cross-over, and mutation processes should be scrutinized to ensure thatsubjects provide reliable data that is uncontaminated by fatigue,inattentive-ness, and bias. Given the need for small search spaces overwhich to search in an IAGA, the mapping of designs to geneticrepresentation takes on greater significance than is typically the casefor a standard genetic algorithm. Binary representations may inflate thesize of the search space and, as such, certain embodiments may use moreefficient M-length strings such as:

αεA₁×A₂× . . . ×A_(M)

where each A_(k) is a finite alphabet of size N_(k) and M typicallycorresponds to the number of sound tokens to be presented.

The best psychometric methods are those that make the most from thefewest number of observations. Applying this principle to the IAGA,duplicates within a generation are deemed wasteful observations, despitethe fact that increasing homogeneity is a desired outcome of an IGA run.The IAGA modifies the standard form of generational updating throughculling, tagging, and selective insertion. The process of cullingremoves duplicates from the next generation. Each member that remains istagged with the number of copies that were removed. In place of theduplicates, unique members are inserted into the generation. The rulesfor such insertion are variable. In certain embodiments, new members(sound tokens) can be drawn from regions of the search space thathaven't yet been explored, while members that have already been rejectedin previous generations may be ruled out (e.g., through a tabu list).

A tabu list is a list of undesirable sound tokens (i.e., sound tokensthat have been correctly identified by the recipient). That is, arunning tabu list may be stored where unfit sound tokens are added tothe tabu list as they are identified. The tabu list may be used to prunethe global population of the search such that tabu sound tokens areremoved from the global population and not included in subsequent phasesof the training.

It may be possible that a recipient correctly identifies a sound tokenthrough error or accident such that he/she still has difficultyperceiving the sound token. As such, in certain embodiments, a soundtoken is added to the tabu list only after the recipient has correctlyidentified the sound token more than once (i.e., two or more times). Thetabu list may persist throughout the duration of an evolution (i.e.,until the end of a specific auditory training session), for a limitednumber of generations, or over multiple evolutions.

An often-reported comment from participants in IGA experiments is thatat the beginning of a run, it is hard for them to accept any option, butas the run persists, it is even harder for them to evaluate among thevery small differences of a nearly homogeneous population. Culling andselective insertion are intended to mitigate the task difficultyencountered in later generations of a run, whereas variable acceptanceis intended to help with task difficulty at the beginning of a run. In astandard IGA, a certain number of parents are necessary to avoidpre-mature convergence of the population. However, the IAGA instructsthe participant to accept as many, or as few, of the members of thecurrent generation as appropriate (i.e., the IAGA does not require a setgeneration size).

At the most general level, the purpose of cross-over is to perpetuateschema in the search space with positive value and to eliminate allothers. Mutation's role is to improve existing schema by introducing newvariations in to the population. Culling and selective insertion (to theextent non-visited portions of the search space have very differentschema) are counter-productive to schema formation, whereas variableacceptance is likely to reinforce larger schema to the detriment ofsmaller ones during the initial phase of an IAGA run.

A natural way to incorporate binary selections (of arbitrary number)with tagging (which preserves the relative dominance of a particularstring within the current generation) into the cross-over operator is togenerate the set of all possible children and sample without replacementfrom the set. Specially, it is assumed that A={α₁, α₂, . . . , α_(k)} isthe set of accepted members from the current generation. The parents (P)are formed from A by augmenting A with each member's collection ofduplicates such that

P={α_(1,1), . . . ,α₁,N₁,α_(2,1), . . . ,α₂,N₂, . . . ,α_(k,1), . . .,α_(k),N_(k)}

The population of potential children (C) is formed by crossing, in allpossible ways, each pair of parents drawn from P. Without replacement,the proper number of strings is drawn to form the next generation. Thesestrings may undergo mutation and then are subject to the culling,tagging, and selective insertion operators. Although the complexity ofthe proposed cross-over operation is larger than most cross-overoperators in standard genetic algorithms, the number of computationsrequired remains relatively small in practice owing to the fact that thesize of the search space is small. In one example, a single-cutcrossover for is employed for search spaces on the order of 2000, suchthat the computation time is negligible when compared with the time ittakes for the recipient to make his/her judgments.

Initialization of IGAs is subject to the same issues encountered with astandard genetic algorithm. However, whereas standard genetic algorithmswork around the problems of initialization by repeated measures, IGAs,in practice, cannot rely on more than a handful of runs. The IAGAutilizes selective insertion to introduce genetic materials into thepopulation that may not have been encountered as well as aninitialization procedure in which values of each parameter are asdistinct across the population as possible.

Both variable acceptance and the culling/tagging operators complicatethe application of standard rules for terminating an IGA run. It hasbeen found that measures of genetic drift, when applied to thepopulation of potential children during crossover, are usefulindicators.

In summary, the IAGA modifies elements of an IGA which may becompromised by the psychometric limitations of the assessment procedure.The IAGA designed to efficiently utilize the recipient's time, promotetheir attentiveness, minimize their fatigue, and work around their bias.The use of culling, tagging, selective insertion, and variableacceptance to achieve these design goals involves a substantialreworking of cross-over, mutation, initialization, and stopping criteriain an IGA or a traditional genetic algorithm. Further details of anexample IAGA are provided in Lineaweaver et al., PsychometricAugmentation of an Interactive Genetic Algorithm for Optimizing CochlearImplant Programs, Proceedings of the 13th Annual Conference on Geneticand Evolutionary Computation 2011, pages 1755-1762, published byAssociation for Computing Machinery (ACM), which is hereby incorporatedby reference herein.

Embodiments have been described above with reference to the use of anIAGA during the auditory training process. However, it is to beappreciated that the other genetic algorithms (e.g., a traditionalgenetic algorithm or an IGA) may be used during an auditory trainingprocess in accordance with alternative embodiments of the presentinvention. Aspects of other genetic algorithms that may be used inaccordance with examples presented herein are described in commonlyowned U.S. Pat. Nos. 6,879,860 and 8,301,259 and commonly owned andco-pending U.S. Patent Publication Nos. 2010/0152813, 2010/0280307,2011/0060383, and 2011/0060702. The content of each of these documentsare hereby incorporated by reference herein.

FIG. 5 is a block diagram illustrating one exemplary auditory trainingsystem 560 that may be used during auditory training of a recipient 562of a cochlear implant 100 (FIG. 1). In the embodiments of FIG. 5,training system 560 comprises a device interface 564, a plurality ofnetwork interfaces 566(A)-566(N), a processor 568, a user interface 570,and a memory 572. User interface 570 comprises a display element 574 andan input element 576, while memory 572 comprises an auditory traininglibrary 580 and auditory training logic 582. Training system 560 may beany type of device capable of executing instructions such as, forexample, a general or special purpose computer (e.g., laptop, desktop,tablet), a mobile device (e.g., personal digital assistant (PDA) ormobile phone), remote control, etc.

In the embodiment illustrated in FIG. 5, training system 560 isconfigured to communicate with cochlear implant 100 via device interface564. That is, device interface 564 is configured to establish a datacommunication link 584 with an interface of cochlear implant 100. Deviceinterface 564 may comprise, for example, a Universal Serial Bus (USB)interface, an Institute of Electrical and Electronics Engineers (IEEE)1394 High Speed Serial Bus (e.g., Firewire) interface, Bluetooth, fixedline, wireless interface, or other type of interface.

Training system 560 also comprises a plurality of network interfaces566(A)-566(N). Network interfaces 566(A)-566(N) may comprise, forexample, Ethernet interfaces, Wi-Fi interfaces, 3rd generation (3G)mobile telecommunications interface, IEEE 802.11 interfaces, IEEE 802.16(WiMAX) interfaces, Bluetooth interfaces, fixed line interfaces, LongTerm Evolution (LTE) interfaces, etc. It is to be that these variousinterfaces may be used in any combination or a single interface may beprovided. Other types of interfaces could also be used in alternativeembodiments.

User interface 570 comprises a display element 574 and an input element576. Display element 574 may be any type of display device, such as, forexample, those commonly used with computer systems. Input element 576may be any type of interface capable of receiving information from arecipient, such as, for example, a computer keyboard, mouse,voice-responsive software, touch-screen (e.g., integrated with displayelement 574), retinal control, joystick, and any other data entry ordata presentation formats now known or later developed.

Memory 572 may comprise read only memory (ROM), random access memory(RAM), magnetic disk storage media devices, optical storage mediadevices, flash memory devices, electrical, optical, or otherphysical/tangible memory storage devices. The processor 568 is, forexample, a microprocessor or microcontroller that executes instructionsfor the auditory training logic 582. Thus, in general, the memory 572may comprise one or more tangible (non-transitory) computer readablestorage media (e.g., a memory device) encoded with software comprisingcomputer executable instructions and when the software is executed (bythe processor 568) it is operable to perform the operations describedherein in connection with the auditory training process and auditorytraining genetic algorithm (through execution of the auditory traininglogic 582).

The auditory training library 580 comprises a collection of sound tokensthat may be played to the recipient 562 during the auditory trainingprocess. As noted above, an auditory training library, such as library580, may include a large number of different types of sound tokens(e.g., language sounds, parts of music, tones, etc.) The training system560 of FIG. 5 is also configured to allow a recipient or other user(e.g., clinician, caregiver, etc.) to add sound tokens that are specificto the recipient. For example, a recipient may have several children andit is important for the recipient to learn to differentiate the voicesof the children. In such embodiments, words, phrases, or other soundsspoken by each of the children may be added to the recipient's auditorytraining library. These recipient-specific sound tokens may beincorporated into the sound training process so that the recipient canlearn to separately identify the different children.

In certain embodiments, the ability to differentiate between children,spouses, co-workers, etc. may be of first importance to the recipient.As such, all or a certain number of the recipient-specific sound tokensmay be selected for the initial generation. In the same or otherembodiments, the recipient-specific sound tokens (particularly thosethat are incorrectly identified by the recipient) may be tagged so thatthere is a greater likelihood these sounds will appear in the trainingprocess.

Recipient-specific sounds may be added to the auditory training library580 in a number of different manners. For example, the training system560 may include a microphone or other auditory input element (not shown)that enables sound to be directly imported into the system.Alternatively, recipient-specific sounds may be recorded on anotherdevice (i.e., voice recorder, computer, etc.) and imported into theauditory training library 580 via a network interface 566(A)-566(N) oranother interface.

In the embodiments of FIG. 5, sound tokens are provided to cochlearimplant 100 via communication link 584 and the sound tokens aresubsequently delivered to the recipient via the cochlear implant. Inalternative embodiments, no communication link may be provided betweencochlear implant 100 and training system 560. In such alternativeembodiments, sound tokens may be played through a speaker system (notshown) that is connected to, or is a part of, training system 560. Inthese embodiments, the cochlear implant 100 receives and processes thesound tokens for delivery to the recipient.

FIG. 5 illustrates an example where the auditory training library 580and auditory training logic 582 are stored and executed locally at acomputing device (auditory training system 560). In other embodiments,auditory training library 580 and auditory training logic 582 may bestored and executed at a remote computing device disposed at a differentlocation (e.g., a remote server) and are accessed through a networkinterface. More specifically, the one of the network interfaces566(A)-566(N) may be configured to communicate with the remote computingdevice via a computing network that may comprise, for example, a localarea network (LAN) or a wide area network (WAN) (e.g., the Internet).

In the same or other embodiments, the results of a genetic algorithmbased auditory training process may be stored within the training system560 and/or uploaded to a data repository (e.g., a cloud computing systemvia the Internet) for future use. In one example, the stored results maybe used, for example, during selection of an initial generation during asubsequent auditory training process. In another example, the storedresults may be (confidentially) shared with clinicians or other users toimproved auditory training processes for the same or other recipients.

An auditory training process in accordance with embodiments of thepresent invention may be administered by a clinician or by the recipientat a remote location. FIGS. 6A-7B illustrative example screens that maybe displayed on user interface 570 in several embodiments of the presentinvention. More specifically, FIG. 6A illustrates an example where asound token corresponding to the sound “ABA” is delivered to recipient562 via cochlear implant 100. After delivery of the sound token, theuser interface 570 displays a message asking the recipient 562 toidentify the sound he/she heard. In this example, a plurality ofpossible sounds are visually displayed at the user interface and therecipient 562 may then select one of the displayed options or choose tolisten to the sound again.

In the example of FIG. 6A, the recipient 562 makes an incorrectselection that results in the user interface 570 displaying a message asshown in FIG. 6B. Since the auditory training process is designed toimprove the recipient's sound comprehension (i.e., teach the recipient562 how to hear through the cochlear implant 100), the training system560 illustrates the correct choice for the recipient. The trainingsystem 560 also gives the recipient 562 the option to listen to thesound again (to reinforce the lesson) or to advance to the next sound.At the time the message of FIG. 6B is displayed to the recipient 562,the training system 560 stores the played sound token as a fit soundtoken. That is, since the recipient 562 failed to correctly identify thesound token, the sound token is selected for use in creating the nextgeneration of sound tokens for delivery to the recipient.

FIG. 7A illustrates an example where a sound token corresponding tofirst and second tones is delivered to recipient 562 via cochlearimplant 100. After delivery of the sound token, the user interface 570displays a message asking the recipient 562 to identify the sound(s)he/she heard. In this example, a plurality of possible sounds arevisually displayed at the user interface and the recipient 562 may thenselect one of the displayed options or listen to the sound again.

In the example of FIG. 7A, the recipient 562 correctly determines thatthe sound token included a first tone that was higher than a secondtone, thereby resulting in the user interface 570 displaying a messageas shown in FIG. 7B. Since the auditory training process is designed toimprove the recipient's sound comprehension, the message notifies therecipient 562 that he/she correctly identified the sound. The trainingsystem 560 also gives the recipient 562 the option to listen to thesound again or to advance to the next sound. At the time the message ofFIG. 7B is displayed to the recipient 562, the training system 560stores the played sound token as an unfit sound token. That is, sincethe recipient 562 correctly identified the sound token, the sound tokenis not selected for use in creating the next generation of sound tokensfor delivery to the recipient.

It is to be appreciated that the displays of FIGS. 6A-7B are merelyillustrative and that many different types of displays and messages maybe used in embodiments of the present invention. For example,alternative embodiments of the present invention may use iconscorresponding to the choices that change in appearance in response tocorrect or incorrect selections by the recipient (e.g., chance color,shape, size, etc.).

Modern cochlear implants use a number of different operating parametersto maximize sound perception and recipient satisfaction. There are alsoa wide variety of fitting options that can be used to customize thesevarious operating parameters for an individual recipient. The task ofthe clinical professional (e.g., clinician or audiologist) is to selecta set of parameters, commonly referred to as a parameter map or, moresimply a MAP, which will provide the best possible sound reception foran individual recipient. In others, when a sound is received at acochlear implant, the recipient's MAP (i.e., selected parameters) isused to process and deliver the sound to the recipient. As such, therecipient's MAP has a profound effect on how a sound is perceived by therecipient.

Examples of parameters that are part of a recipient's MAP include, forexample, the speech strategy implemented in the cochlear implant.Additionally, within any given speech strategy a great many parametersand parameter values may be specified to tailor the encoding andstimulation for an individual recipient. Examples of parameters that maybe selected for a speech strategy include but are not limited to thenumber of channels of stimulation represented, the configuration andnumber of intracochlear and/or extracochlear electrodes which are to beassociated with each channel, the pulse repetition rate for eachchannel, the pulse pattern, the width of each pulse or between pulses,the number of spectral maxima periodically chosen for representation,the mapping of sound pressure to stimulus current for each channel(threshold levels, comfort levels and compression curves), the frequencyboundaries allocated for each channel, parameters for the front endfiltering of the audio from the microphone (pre-emphasis), an automaticgain control threshold, channel-specific compression ratios, and attackand release times. Additional parameters may include, but are notlimited to, loudness parameters such as long term loudness balance (thatis, electrical and mechanical gains), parameters for short term gainmanipulations, particularly signal-dependent gain adjustments. Such gainadjustment parameters include, for example, parameters for adjustmentsto minimize cross-modal masking, and adjustments to emphasize speechfeatures such as noise, frication or voicing. Further parameters mayinclude frequency domain parameters, time domain parameters, and/orbinaural parameters. As one skilled in the art would appreciate, theabove parameters are examples of parameters which may be selected andtailored to optimally fit a cochlear implant to a recipient.

Because there may be thousands of possible parameter maps, it isimpractical for a recipient to experience all of the alternatives and toevaluate the performance of each alternative. Similarly, it is notpossible to identify an optimal parameter map by prescription based on alimited set of measurements as is, for example, the case in fittingeyeglasses. Because parameters of cochlear implant systems ofteninteract non-linearly and non-monotonically, it is also not possible tosequentially optimize parameters one at a time, adjusting each insuccession to its optimal value.

In order for a recipient to hear sounds through the cochlear implant(and to be able to participate in the auditory training processdescribed above) the recipient must receive a MAP that is used todeliver the sound tokens to the recipient. The process of selecting arecipient's MAP is often referred to as “fitting” and there are avariety of approaches for fitting the cochlear implant systems to arecipient. In one embodiment, a clinician may simply set the parametersof the MAP to default values regardless of the individual recipient. Inanother embodiment, preferred MAPs which have been experimentallydetermined to be generally good, if not best, for many or mostrecipients may be selected. Such preferred MAPs may be based on personalexperience, published performance data, or intuition. Some cliniciansevaluate a limited set of alternatives adjusting individual parametersbased upon measured perceptual limitations and inferred relationshipsamong the parameters.

In certain embodiments, a genetic algorithm may be used to select therecipient's MAP for use during the auditory training process. The use ofa genetic algorithm to select a recipient's MAP is described in commonlyowned U.S. Pat. Nos. 6,879,860 and 8,301,259 and commonly owned andco-pending U.S. Patent Publication Nos. 2010/0152813, 2010/0280307,2011/0060383, and 2011/0060702. The content of each of these documentsare hereby incorporated by reference herein.

In the above described embodiments, the recipient's parameter values(i.e., MAP) remain fixed so that the MAP parameters do not change duringthe auditory training process. As such, the recipient's hearingperception with the pre-selected parameter values (i.e., the selectedMAP) improves over the course of the auditory training process. That is,the auditory training process teaches the recipient's brain to best usethe MAP that was selected during the earlier fitting process.

In certain embodiments of the present invention, it may be discoveredduring the auditory training process described above that therecipient's current MAP is not well suited for the recipient or that theMAP needs some adjustment. In such embodiments, another fitting processmay be implemented wherein the recipient's MAP is changed or refined.

FIG. 8 is a flowchart of a method 800 in accordance with embodiments ofthe present invention. Method 800 begins at block 802 where arecipient's initial MAP is set or selected using, for example, a geneticalgorithm based fitting process. At block 804, an initial generation ofsound tokens is selected for use in an auditory training process. Theselection of this initial generation may be performed in a number ofdifferent manners as described elsewhere herein. For example, theinitial sound tokens may be selected at random from an auditory traininglibrary, the initial sound tokens may be selected from only a subset ifthe auditory training library, the initial sound tokens may be selectedby an audiologist/clinician based on experience, suggestions andrecommendations from others, etc.

Next, at block 806 a fitness function is executed to evaluate theinitial generation. That is, the initial sound tokens are sequentiallyplayed to the recipient through the cochlear implant (e.g., via adedicated connection or indirectly over speakers). As described above,after each sound token is played the recipient is asked to identify thesound token. In one embodiment, the auditory training is performed by,or in the presence of, a clinician and the clinician receives therecipient's identification of the sound token. The clinician may providethe recipient with several visual choices (e.g., in a book, on a screenof a computing device, etc.) and the recipient is instructed to selectwhich possible choice is closest to the sound that he/she perceived. Inanother embodiment, the auditory training is performed by the recipienton a computing device at a remote location (i.e., outside of aclinician's office). In such embodiments, the computing device isconfigured to visually display several choices and the user selectswhich possible choice is closest to the sound that he/she perceived.

After execution of the fitness function for all or a selected number ofsound tokens in the initial generation, a determination is made at block808 as to whether the recipient's MAP is satisfactory. For example, ifthe user is unable to correctly identify a certain number (e.g., amajority) of the sound tokens, it may be determined that the recipient'sinitial MAP is unsatisfactory and not well-suited for the recipient.

It is to be appreciated that a number of different methods may be usedto determine if a MAP is unsatisfactory. For example, the failure of arecipient to identify certain sounds may indicate MAP problems. In oneembodiment, such sounds may be added to the library as markers and afailure to identify one or more of these sounds may be viewed as anindication that there is a MAP problem.

If it is determined that the recipient's MAP is satisfactory, then asecond determination is made at block 810 as to whether the geneticalgorithm (and the auditory training) should be stopped (i.e., whetherstopping criteria have been satisfied). In one embodiment, the auditorytraining is stopped after a predetermined number of generations. Inanother embodiment, the auditory training is stopped after apredetermined period of time.

If the stopping criteria have been satisfied, then auditory trainingends at block 812. Otherwise, at block 814, a new generation is createdthrough reproduction as described elsewhere herein. The reproductionoccurs using only those sound tokens that the recipient failed tocorrectly identify. After creation of the new generation, the fitnessfunction is executed at block 806 on the new generation.

Returning to block 808, if it is determined that the recipient's MAP isunsatisfactory, then at block 816 a new MAP is selected for therecipient. In this embodiment, a genetic algorithm based fitting processis used to select the recipient's new MAP. In certain embodiments, thegenetic algorithm based fitting process uses the auditory trainingresults in the selection of the new MAP.

After selection of the recipient's new MAP, at block 818 a newgeneration of sound tokens is selected for further auditory training. Inone embodiment, this new generation comprises the initial generationthat was previously delivered to the recipient.

After creation of the new generation, the fitness function is executedagain at block 806. The above process continues until satisfaction ofthe stopping criteria and/or the recipient's MAP is deemed to besatisfactory.

Cochlear implant fitting and auditory training processes may be timeconsuming and difficult for a recipient. As such, method 800 describedabove with reference to FIG. 8 may be performed over a number ofdifferent sessions.

The invention described and claimed herein is not to be limited in scopeby the specific preferred embodiments herein disclosed, since theseembodiments are intended as illustrations, and not limitations, ofseveral aspects of the invention. Any equivalent embodiments areintended to be within the scope of this invention. Indeed, variousmodifications of the invention in addition to those shown and describedherein will become apparent to those skilled in the art from theforegoing description. Such modifications are also intended to fallwithin the scope of the appended claims.

What is claimed is:
 1. A method comprising: selecting an initial set ofauditory training sound tokens from an auditory training library;presenting the initial set of sound tokens to a recipient via a hearingprosthesis; receiving recipient feedback in response to the presentedsound tokens, wherein the feedback indicates the recipient's perceptionof the sound tokens; and executing a genetic algorithm based on therecipient's perception of the sound tokens to create a second set ofauditory sound tokens for presentation to the recipient.
 2. The methodof claim 1, further comprising: sequentially presenting the initial setof sound tokens to the recipient via the hearing prosthesis; andreceiving recipient feedback in response to the presented sound tokensafter each sequentially presented sound token.
 3. The method of claim 2,wherein each sound token corresponds to a known sound, and whereinreceiving recipient feedback in response to a first presented soundtoken comprises: receiving an indication of whether the recipientcorrectly perceived the known sound corresponding to the first presentedsound token.
 4. The method of claim 3, further comprising: if therecipient correctly perceived the known sound corresponding to the firstpresented sound token, eliminating the first presented sound token foruse in creation of the second set of auditory sound tokens.
 5. Themethod of claim 3, further comprising: presenting a visualrepresentation of a plurality of sounds to the recipient; presenting arequest for the recipient to select one of the visual representationsthat corresponds to the sound token; and receiving a selection from therecipient of one of the visual representations.
 6. The method of claim1, wherein selecting the initial set of auditory training sound tokensfrom the auditory training library comprises: selectingrecipient-specific sound tokens added to the auditory training libraryby a user.
 7. The method of claim 1, wherein selecting the initial setof auditory training sound tokens from the auditory training librarycomprises: selecting the initial sound tokens at random from theauditory training library.
 8. The method of claim 1, wherein presentingthe initial set of sound tokens to the recipient via the hearingprosthesis comprises: presenting the initial set of sound tokens to therecipient via a cochlear implant.
 9. The method of claim 1, whereinexecuting a genetic algorithm based on the recipient's perception of thesound tokens to create a second set of auditory sound tokens comprises:executing an interactive augmented genetic algorithm.
 10. A methodcomprising: selecting a set of parameter values for a cochlear implant;providing the set of parameter values to the cochlear implant for use indelivering stimulation to a recipient; and executing a genetic algorithmbased auditory training process in which sound tokens are processed anddelivered to the recipient in accordance with the set of parametervalues.
 11. The method of claim 10, wherein selecting the set ofparameter values for the cochlear implant comprises: executing a geneticalgorithm based fitting process to select the parameter values.
 12. Themethod of claim 10, wherein executing a genetic algorithm based auditorytraining process comprises: selecting an initial set of auditorytraining sound tokens from an auditory training library; presenting theinitial set of sound tokens to a recipient via the cochlear implant;receiving recipient feedback in response to the presented sound tokens,wherein the feedback indicates the recipient's perception of the soundtokens; and executing a genetic algorithm based on the recipient'sperception of the sound tokens to create a second set of auditory soundtokens for presentation to the recipient.
 13. The method of claim 12,further comprising: sequentially presenting the initial set of soundtokens to the recipient via the cochlear implant; and receivingrecipient feedback in response to the presented sound tokens after eachsequentially presented sound token.
 14. The method of claim 13, whereineach sound token corresponds to a known sound, and wherein receivingrecipient feedback in response to a first presented sound tokencomprises: receiving an indication of whether the recipient correctlyperceived the known sound corresponding to the first presented soundtoken.
 15. The method of claim 14, further comprising: if the recipientcorrectly perceived the known sound corresponding to the first presentedsound token, eliminating the first presented sound token for use increation of the second set of auditory sound tokens.
 16. The method ofclaim 10, further comprising: during the genetic algorithm basedauditory training process, determining that the selected set ofparameter values for the cochlear implant is unsatisfactory.
 17. Themethod of claim 16, further comprising: selecting a new set of parametervalues for the cochlear implant based on results from the geneticalgorithm based auditory training process.
 18. An auditory trainingsystem, comprising: a user interface; and a processor configured to:present the initial set of sound tokens to a recipient of a hearingprosthesis; receive recipient feedback in response to the presentedsound tokens, wherein the feedback indicates the recipient's perceptionof the sound tokens; and execute an interactive augmented geneticalgorithm genetic algorithm based on the recipient's perception of thesound tokens to create a second set of auditory sound tokens forpresentation to the recipient.
 19. The system of claim 18, furthercomprising: a memory comprising an auditory training library, whereinthe processor is configured to select the initial set of sound tokensfrom the auditory training library.
 20. The method of claim 18, whereinthe auditory training library comprises recipient-specific sound tokensadded to the auditory training library by a user.
 21. The system ofclaim 18, wherein the processor is configured to: sequentially presentthe initial set of sound tokens to the recipient via the hearingprosthesis; and receive recipient feedback in response to the presentedsound tokens after each sequentially presented sound token.
 22. Thesystem of claim 21, wherein each sound token corresponds to a knownsound, and wherein to receive recipient feedback in response to a firstpresented sound token the processor is configured to: receive anindication of whether the recipient correctly perceived the known soundcorresponding to the first presented sound token.
 23. The system ofclaim 22, wherein if the recipient correctly perceived the known soundcorresponding to the first presented sound token, the processor isconfigured to add the first presented sound token to a tabu list. 24.The system of claim 23, wherein the processor is configured to: presenta visual representation of a plurality of sounds to the recipient;present a request for the recipient to select one of the visualrepresentations that corresponds to the sound token; and receive aselection from the recipient of one of the visual representations. 25.One or more computer readable storage media encoded with softwarecomprising computer executable instructions and when the software isexecuted operable to: sequentially present an initial set of soundtokens to a recipient of a hearing prosthesis; receive recipientfeedback in response to each presented sound token, wherein the feedbackindicates whether the recipient correctly perceived known soundscorresponding to each the presented sound token; execute a geneticalgorithm based on the recipient feedback to create a second set ofauditory sound tokens for presentation to the recipient.
 26. Thecomputer readable storage media of claim 25, further comprisinginstructions operable to: after each presentation of a first soundtoken, present a visual representation of a plurality of sounds to therecipient; present a request for the recipient to select one of thevisual representations that corresponds to the first sound token; andreceive a selection from the recipient of one of the visualrepresentations.
 27. The computer readable storage media of claim 25,further comprising instructions operable to: add recipient-specificsound tokens generated by a user to an auditory training library. 28.The computer readable storage media of claim 25, wherein theinstructions operable to execute the genetic algorithm compriseinstructions operable to: execute an interactive augmented geneticalgorithm.